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A novel method called mixed variable system Monte Carlo tree search (MVSMCTS)formulation is presented for optimization problems considering various types ofvariables with single and mixed continuous-discrete system. This methodutilizes a reinforcement learning algorithm with improved Monte Carlo treesearch (IMCTS) formulation. For sizing and shape optimization of trussstructures, the design variables are the cross-sectional areas of the membersand the nodal coordinates of the joints. MVSMCTS incorporates update processand accelerating technique for continuous variable and combined scheme forsingle and mixed system. Update process indicates that once a solution isdetermined by MCTS with automatic mesh generation in continuous space, it isused as the initial solution for next search tree. The search region should beexpanded from the mid-point, which is the design variable for initial state.Accelerating technique is developed by decreasing the range of search regionand the width of search tree based on the number of meshes during updateprocess. Combined scheme means that various types of variables are coupled inonly one search tree. Through several examples, it is demonstrated that thisframework is suitable for mixed variable structural optimization. Moreover, theagent can find optimal solution in a reasonable time, stably generates anoptimal design, and is applicable for practical engineering problems.

Mixed variable structural optimization using mixed variable system Monte Carlo tree search formulation